{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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s8sfI/zTfntr8C+DzwHeb7TuB63qJ0Mx6MdHv/JJOaVboPQw8C/wEeDciPmiecgDY3E+IZtaHiZI/Ij6MiC3A+cBW4NNrPW2ttpK2SVqWtLyyspKP1Mw6NdXd/oh4F/hX4Apgg6RjNwzPB95sabM9IpYiYmlhYWGWWM2sQ2OTX9InJG1oHp8O/C6wD/g+8AfN024Gnu4rSDPr3iQDezYBOyWdwug/i8cj4p8k/Qh4VNJfAv8GPDRZl20De7odCDJwIacH9dX6Bhyf08/ZTR4016zthEx+osYmf0TsAS5bY/sbjH7/N7OTkP/Cz6xSTn6zSjn5zSrl5DerlJPfrFIqjYzrvDPpLeC/mm/PAX4+WOftHMfxHMfxTrY4fjMiPjHJAQdN/uM6lpYjYmkunTsOx+E4/LHfrFZOfrNKzTP5t8+x79Ucx/Ecx/F+beOY2+/8ZjZf/thvVqm5JL+kqyX9h6TXJd0+jxiaOPZLelXSbknLA/a7Q9JhSXtXbdso6VlJP26+nj2nOO6S9N/NOdkt6ZoB4rhA0vcl7ZP0mqQ/abYPek4KcQx6TiR9XNIPJb3SxPEXzfaLJL3QnI/HJJ02U0cRMeg/4BRG04B9CjgNeAW4dOg4mlj2A+fMod/PAZcDe1dt+yvg9ubx7cA35xTHXcCfDnw+NgGXN4/PBP4TuHToc1KIY9Bzwmhc7hnN41OBFxhNoPM4cEOz/VvAH83Szzyu/FuB1yPijRhN9f0ocO0c4pibiHgeePuEzdcymggVBpoQtSWOwUXEwYh4uXn8PqPJYjYz8DkpxDGoGOl90tx5JP9m4Gervp/n5J8BfE/SS5K2zSmGY86LiIMwehMC584xllsl7Wl+Lej914/VJF3IaP6IF5jjOTkhDhj4nAwxae48kn+tqUbmVXK4MiIuB34f+Lqkz80pjvXkAeBiRms0HATuGapjSWcATwC3RcR7Q/U7QRyDn5OYYdLcSc0j+Q8AF6z6vnXyz75FxJvN18PAU8x3ZqJDkjYBNF8PzyOIiDjUvPGOAg8y0DmRdCqjhHs4Ip5sNg9+TtaKY17npOl76klzJzWP5H8RuKS5c3kacAOwa+ggJC1KOvPYY+CLwN5yq17tYjQRKsxxQtRjyda4ngHOiUaTMT4E7IuIe1ftGvSctMUx9DkZbNLcoe5gnnA38xpGd1J/AvzZnGL4FKNKwyvAa0PGATzC6OPj/zH6JHQL8BvAc8CPm68b5xTH3wOvAnsYJd+mAeL4bUYfYfcAu5t/1wx9TgpxDHpOgN9iNCnuHkb/0fz5qvfsD4HXgX8EPjZLP/4LP7NK+S/8zCrl5DerlJPfrFJOfrNKOfnNKuXkN6uUk9+sUk5+s0r9PyhPkvaabPDEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()\n",
    "\n",
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image\n",
    "\n",
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.196693\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 4.494127 analytic: 4.494127, relative error: 5.607352e-11\n",
      "numerical: -10.901029 analytic: -10.901029, relative error: 2.282183e-11\n",
      "numerical: -30.532222 analytic: -30.532222, relative error: 7.593504e-12\n",
      "numerical: -4.674799 analytic: -4.667840, relative error: 7.448297e-04\n",
      "numerical: -6.794198 analytic: -6.794198, relative error: 8.103359e-11\n",
      "numerical: -32.056923 analytic: -32.056923, relative error: 5.682782e-12\n",
      "numerical: 17.653653 analytic: 17.653653, relative error: 9.973916e-12\n",
      "numerical: 17.171466 analytic: 17.171466, relative error: 3.192452e-11\n",
      "numerical: -23.563201 analytic: -23.563201, relative error: 4.850846e-12\n",
      "numerical: 6.756347 analytic: 6.756347, relative error: 1.145921e-10\n",
      "numerical: -18.993497 analytic: -18.993497, relative error: 1.033455e-11\n",
      "numerical: 0.933799 analytic: 0.933799, relative error: 1.622967e-10\n",
      "numerical: 16.858082 analytic: 16.858082, relative error: 1.023787e-12\n",
      "numerical: 20.266158 analytic: 20.266158, relative error: 6.322908e-12\n",
      "numerical: -12.615020 analytic: -12.615020, relative error: 6.756834e-12\n",
      "numerical: -51.299999 analytic: -51.299999, relative error: 6.516425e-12\n",
      "numerical: 32.105866 analytic: 32.070679, relative error: 5.482743e-04\n",
      "numerical: -1.136542 analytic: -1.136542, relative error: 1.258065e-10\n",
      "numerical: 5.971831 analytic: 5.971831, relative error: 5.193730e-12\n",
      "numerical: -11.204337 analytic: -11.220445, relative error: 7.183143e-04\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1**\n",
    "\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*  Loss函数有断点，不严格可微？？？对8起，我8会\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.196693e+00 computed in 0.195896s\n",
      "9.196692892958062\n",
      "Vectorized loss: 9.196693e+00 computed in 0.002235s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.143489s\n",
      "Vectorized loss and gradient: computed in 0.002781s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 794.313556\n",
      "iteration 100 / 1500: loss 288.804173\n",
      "iteration 200 / 1500: loss 107.735931\n",
      "iteration 300 / 1500: loss 42.442376\n",
      "iteration 400 / 1500: loss 18.503214\n",
      "iteration 500 / 1500: loss 10.587709\n",
      "iteration 600 / 1500: loss 7.108554\n",
      "iteration 700 / 1500: loss 5.991741\n",
      "iteration 800 / 1500: loss 5.693539\n",
      "iteration 900 / 1500: loss 5.140331\n",
      "iteration 1000 / 1500: loss 4.993475\n",
      "iteration 1100 / 1500: loss 5.244564\n",
      "iteration 1200 / 1500: loss 5.300461\n",
      "iteration 1300 / 1500: loss 5.399649\n",
      "iteration 1400 / 1500: loss 5.981620\n",
      "That took 3.647000s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.365755\n",
      "validation accuracy: 0.379000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 405.670343\n",
      "iteration 100 / 1500: loss 242.130715\n",
      "iteration 200 / 1500: loss 147.450865\n",
      "iteration 300 / 1500: loss 90.639340\n",
      "iteration 400 / 1500: loss 57.559951\n",
      "iteration 500 / 1500: loss 35.987712\n",
      "iteration 600 / 1500: loss 24.040426\n",
      "iteration 700 / 1500: loss 16.744450\n",
      "iteration 800 / 1500: loss 11.799239\n",
      "iteration 900 / 1500: loss 8.807291\n",
      "iteration 1000 / 1500: loss 7.511146\n",
      "iteration 1100 / 1500: loss 6.592900\n",
      "iteration 1200 / 1500: loss 5.852318\n",
      "iteration 1300 / 1500: loss 5.463928\n",
      "iteration 1400 / 1500: loss 5.258690\n",
      "iteration 0 / 1500: loss 786.461982\n",
      "iteration 100 / 1500: loss 286.110989\n",
      "iteration 200 / 1500: loss 107.194860\n",
      "iteration 300 / 1500: loss 42.713756\n",
      "iteration 400 / 1500: loss 18.915384\n",
      "iteration 500 / 1500: loss 10.776709\n",
      "iteration 600 / 1500: loss 6.880899\n",
      "iteration 700 / 1500: loss 5.921510\n",
      "iteration 800 / 1500: loss 5.438603\n",
      "iteration 900 / 1500: loss 5.394158\n",
      "iteration 1000 / 1500: loss 5.371245\n",
      "iteration 1100 / 1500: loss 4.780323\n",
      "iteration 1200 / 1500: loss 5.315209\n",
      "iteration 1300 / 1500: loss 5.223916\n",
      "iteration 1400 / 1500: loss 5.717031\n",
      "iteration 0 / 1500: loss 406.793614\n",
      "iteration 100 / 1500: loss 20.352483\n",
      "iteration 200 / 1500: loss 18.535142\n",
      "iteration 300 / 1500: loss 18.418560\n",
      "iteration 400 / 1500: loss 26.594159\n",
      "iteration 500 / 1500: loss 32.027847\n",
      "iteration 600 / 1500: loss 17.266804\n",
      "iteration 700 / 1500: loss 23.808740\n",
      "iteration 800 / 1500: loss 22.375841\n",
      "iteration 900 / 1500: loss 13.257811\n",
      "iteration 1000 / 1500: loss 20.498835\n",
      "iteration 1100 / 1500: loss 12.658351\n",
      "iteration 1200 / 1500: loss 26.851852\n",
      "iteration 1300 / 1500: loss 12.219900\n",
      "iteration 1400 / 1500: loss 13.499866\n",
      "iteration 0 / 1500: loss 785.726692\n",
      "iteration 100 / 1500: loss 22.029815\n",
      "iteration 200 / 1500: loss 32.917525\n",
      "iteration 300 / 1500: loss 32.503136\n",
      "iteration 400 / 1500: loss 17.198352\n",
      "iteration 500 / 1500: loss 25.003817\n",
      "iteration 600 / 1500: loss 17.811543\n",
      "iteration 700 / 1500: loss 16.573043\n",
      "iteration 800 / 1500: loss 22.321352\n",
      "iteration 900 / 1500: loss 20.572771\n",
      "iteration 1000 / 1500: loss 20.499822\n",
      "iteration 1100 / 1500: loss 35.294282\n",
      "iteration 1200 / 1500: loss 17.261638\n",
      "iteration 1300 / 1500: loss 30.995473\n",
      "iteration 1400 / 1500: loss 28.499560\n",
      "lr 1.000000e-07 reg 2.500000e+04 train accuracy: 0.376857 val accuracy: 0.382000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.370224 val accuracy: 0.385000\n",
      "lr 5.000000e-06 reg 2.500000e+04 train accuracy: 0.189837 val accuracy: 0.190000\n",
      "lr 5.000000e-06 reg 5.000000e+04 train accuracy: 0.218490 val accuracy: 0.240000\n",
      "best validation accuracy achieved during cross-validation: 0.385000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.39 on the validation set.\n",
    "\n",
    "#Note: you may see runtime/overflow warnings during hyper-parameter search. \n",
    "# This may be caused by extreme values, and is not a bug.\n",
    "\n",
    "learning_rates = [1e-7, 5e-5]  # 第二个lr要加一个小数点才能收敛，不然就炸了\n",
    "regularization_strengths = [2.5e4, 5e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train, y_train, learning_rate=lr, reg=rs,\n",
    "                      num_iters=1500, verbose=True)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_acc = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_acc = np.mean(y_val == y_val_pred)\n",
    "        results[(lr, rs)] = (train_acc, val_acc)\n",
    "        if (val_acc > best_val):\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap=plt.cm.coolwarm)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.370000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "      \n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline question 2**\n",
    "\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in*  you can treat svm classifier as template matching\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
